Harnessing the potential of trace data and linguistic analysis to predict learner performance in a multi‐text writing task-Reference-Cited by-同舟云学术

Harnessing the potential of trace data and linguistic analysis to predict learner performance in a multi‐text writing task

Published:2022-12-13 Issue: Volume: Page:
ISSN:0266-4909
Container-title:Journal of Computer Assisted Learning
language:en
Short-container-title:Computer Assisted Learning

Author:

Raković Mladen¹^ORCID,Iqbal Sehrish¹,Li Tongguang¹,Fan Yizhou²^ORCID,Singh Shaveen¹,Surendrannair Surya¹,Kilgour Jonathan²,van der Graaf Joep³,Lim Lyn⁴,Molenaar Inge³,Bannert Maria⁴,Moore Johanna²,Gašević Dragan¹²⁵

Affiliation:

1. Centre for Learning Analytics Monash University Melbourne Victoria Australia

2. School of Informatics The University of Edinburgh Edinburgh UK

3. Behavioural Science Institute Radboud University Nijmegen Netherlands

4. School of Education Technical University of Munich Munich Germany

5. Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Funder

Deutsche Forschungsgemeinschaft

Economic and Social Research Council

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Australian Research Council

Publisher

Wiley

Subject

Computer Science Applications,Education

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcal.12769

Reference84 articles.

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2. Inter-Coder Agreement for Computational Linguistics

3. Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning

4. Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges

5. Measuring Cognitive and Metacognitive Regulatory Processes During Hypermedia Learning: Issues and Challenges

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